A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition

A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition
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使用基于神经网络的算法进行手写数字识别的调查

DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Ammara Zamir
Ammara Zamir
中科院分区：
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文献类型：
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作者：
Muhammad Ramzan;H. Khan;S. Awan;Waseem Akhtar;Mahwish Ilyas;Ahsan Mahmood;Ammara Zamir

文献摘要

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手写内容的检测和识别是将非智能信息（如图像）转换为机器可编辑文本的过程。该研究领域已成为一个活跃的研究领域，由于在许多领域的广泛应用，如手写的表格或文件在银行，考试表格由学生填写，用户的身份验证应用。一般来说，手写体内容识别过程包括四个步骤：数据预处理、分割、特征提取和选择、应用监督学习算法。本文对手写数字识别（HWDR）的现有技术进行了详细的调查。这篇综述是新颖的，因为它是集中在HWDR，也只讨论了神经网络（NN）及其改进算法的应用。我们讨论了神经网络的概述和不同的算法，已通过神经网络。此外，本研究还对NN及其变体用于数字识别进行了详细的调查。每一个现有的工作，我们阐述了它的步骤，新奇，使用的数据集和优点和局限性。此外，我们提出了一个科学计量分析HWDR，提出了顶级期刊和研究内容的来源在这一研究领域。我们还提出了研究挑战和潜在的未来工作。
The detection and recognition of handwritten content is the process of converting non-intelligent information such as images into machine edit-able text. This research domain has become an active research area due to vast applications in a number of fields such as handwritten filing of forms or documents in banks, exam form filled by students, users’ authentication applications. Generally, the handwritten content recognition process consists of four steps: data preprocessing, segmentation, the feature¬ extraction and selection, application of supervised learning algorithms. In this paper, a detailed survey of existing techniques used for Hand Written Digit Recognition(HWDR) is carried out. This review is novel as it is focused on HWDR and also it only discusses the application of Neural Network (NN) and its modified algorithms. We discuss an overview of NN and different algorithms which have been adopted from NN. In addition, this research study presents a detailed survey of the use of NN and its variants for digit recognition. Each existing work, we elaborate its steps, novelty, use of dataset and advantages and limitations as well. Moreover, we present a Scientometric analysis of HWDR which presents top journals and sources of research content in this research domain. We also present research challenges and potential future work.